For the agent network, the linear value decomposition [1]} is applied to deduce the contribution of each agent in the presence of only a group reward.
Since followers make decisions based on their leaders' intentions in IS-LFF, we decompose the joint action-value function into dependent individual value functions, i.e., \(Q_{tot}=\sum _{i=1}^n Q_i(h_i,aggr_i)\) .
For each agent, the individual value function is conditioned on its leaders' actions, alleviating the environment non-stationarity due to the agents’ changing policies.
